Imaging telescope or lens: Explore Scientific ED127 Air-Spaced Triplet Apochromatic Refractor
Imaging camera: Canon 550D
Mount: Losmandy G-11
Guiding telescope or lens: Orion ShortTube 80mm f/5.0
Guiding camera: Orion StarShoot AutoGuider
Focal reducer: Astro-Tech Field Flattener
Filter: Hutech IDAS 2" LPS-D1
Accessory: DewBuster Dew Controller
Hutech IDAS 2" LPS-D1: 43x180" ISO1600
Integration: 4.9 hours
Avg. Moon age: 9.48 days
Avg. Moon phase: 16.75%
Astrometry.net job: 1137392
RA center: 198.957 degrees
DEC center: 42.031 degrees
Pixel scale: 0.930 arcsec/pixel
Orientation: 173.819 degrees
Field radius: 0.489 degrees
Locations: Home base, Yorktown, VA, United States
The Sunflower Galaxy M63 is a spiral galaxy 23 million miles away in the direction of Canes Venatici. I had planned to acquire much more data this object, but the persistent deck of clouds which has loomed over coastal Virginia for these past three months has thwarted all opportunities. Seriously, I’ve become more of a cloudgazer than a stargazer this summer. Next spring I’ll pick back up with M63, but I figured I might as well have a look at what I acquired. One major difference in pre-processing than I’ve done in the past: I used the PixInsight SubframeSelector to weight the images by their star FWHM rather than the usual noise evaluation. The weighting expression I chose looked like this:
(1-((weightMax - weightMin)/(FWHMmax-FWHMmin))*(FWHM-FWHMmin)+weightMin)/(weightMin + weightMax)
The expression is simply (one minus) the equation of a line where FWHM is the independent variable and the image weight is the dependent variable. The values weightMin and weightMax decide the smallest and largest weights to assign to a given image. Everything is normalized to unity, so I chose 0.05 and 1.0 as the min and max weights. The values FWHMmin and FWHMmax are the minimum and maximum start FWHM out of the entire set of subframes which I read from the SubframeSelector table. Using this expression allowed the subframe with the largest FWHM to contribute just 5% to the weighted average and the subframe with the smallest FWHM to contribute 100%. The mean value of the weights was around 0.65, with the subframe with the largest FWHM being a large outlier. I probably could have tossed that one. It was nice to see smaller stars in the integrated image relative to the default noise evaluation weighting scheme.
Processing followed much of the same steps of my M31 image. I worked hard to retain star colors (and in some sense shove colors back into them). Altogether, I’d rate this image as just o.k. I am becoming disheartened by the amount of integration time required given the ambient light pollution in my area and the sparse opportunities for cloud-free dark nights. This image could use a lot more signal, but it will be a long haul before I can collect it all.
Processing Workflow (PixInsight)
1. Initial crop (Dynamic crop) .
2. Reduce light pollution gradients (DynamicBackgroundExtraction, subtract)
3. Neutralize background (BackgroundNeutralization).
4. Set white balance (ColorCalibration; use entire image including galaxy).
5. Non-linear stretch (HistogramTransformation, lower midtones slider aggressively).
6. Non-linear stretch (HistogramTransformation, raise blackpoint slider).
7. Set luminance coefficients to 0.333333 for RGB channels (RGBWorkingSpace).
8. Increase contrast (CurvesTransformation “s” curve).
9. Restore colors and smooth star profiles to the heavily stretched stars using the method described by Vincent Peris (see Peris reference below).
10. Distribute color into star cores by duplicating image, blurring the duplicate with Convolution, increasing color saturation with CurvesTransformation and then blending the result into the original image through a star mask with the PixelMath expression: $T*0.4 + 0.6*M63_blurred.
11. Reduce luminance noise (ACDNR to luminance with luminance mask).
12. Reduce chrominance noise (ACDNR to chrominance with strong luminance mask).
13. Lower black point (HistogramTransformation).
14. Small contrast “S” curve (CurvesTransformation)
15. Reduce green (SCNR to green, 90%).
16. Increase galaxy color saturation (CurvesTransformation w/mask selecting galaxy only).
17. Increase star color saturation (CurvesTransformation w/inverted star mask).
18. Mild chrominance noise reduction, particularly in the galaxy where saturation brought up some noise (ACDNR to chrominance w/ luminance mask).
19. Sharpen galaxy details (MultiscaleMedianTransform, bias 0.1 to layer 4; w/mask selecting galaxy only).
20. Reduce star sizes (MorphologicalTransformation, morphological selection using size 5 diamond structuring element w/star mask selecting stars).
21. Sharpen stars (MultiscaleMedianTransform, bias 0.1 to layer 4; w/inverted star mask).
22. Balance median RGB values of background by lowing black point slightly (HistogramTransformation)
23. Lower median background value to 0.11 (CurvesTransformation).
24. Final crop to 5x7 aspect ratio (DynamicCrop)
25. Set ICC profile to sRGB for web publishing (ICCProfileTransformation).
“Dynamic Range and Local Contrast” software tutorial by Vincent Peris:
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